This page provides an overview of regional forecast skill for the SON 2025 period. Forecast scores are updated automatically every week throughout the competitive period. The current data includes 5 6 forecasts initialized between Thursday 14th August 2025 and Thursday 11th 18th September 2025 (inclusive). For a detailed description of the outputs, please refer to the section's overview.
...
| Expand |
|---|
| title | Forecast window 1 (days 19 to 25) |
|---|
|
| Team name | Team rank | Model name | Model rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | Asia | Oceania |
|---|
| CMAandFDUMicroEnsemble | 1 | FengshunAdjustMicroDuet | 1 | 0.072073 | 0.142116 | 0.021032 | 0.005053 | -0.016024- | 0.026021 | 0.008018 | 0.034044 | 0.146082 | 0.107072 | 0.057069 | 0.088117 | | CMAandFDUMicroEnsemble | 1 | FengshunHybridStillLearning | 3 | 0.07067 | 0.126108 | 0.026029 | 0.01048 | 0.009018 | -0.005017- | 0.005019 | 0.05039 | 0.097071 | 0.106097 | 0.066059 | 0.061125 | | CMAandFDUMicroEnsemble | 1 | FengshunHuracan | 109 | 0.017027 | 0.041032 | 0.006019 | -0.023035 | 0.003014 | -0.049008 | 0.01014 | 0.013038 | 0.016006 | -0.006036 | 0.048039 | -0.016043 | | MicroEnsembleCMAandFDU | 2 | MicroDuetFengshunAdjust | 2 | 0.07207 | 0.11614 | 0.033017 | 0.047016 | -0.019016 | -0.01202 | 0.022013 | 0.032038 | 0.087138 | 0.075105 | 0.07046 | 0.113087 | | MicroEnsembleCMAandFDU | 2 | StillLearningFengshunHybrid | 4 | 0.067065 | 0.109119 | 0.029019 | 0.04302 | 0.012008 | 0.006002 | -0.025002 | 0.02406 | 0.078096 | 0.101 | 0.059045 | 0.116044 | | MicroEnsembleCMAandFDU | 2 | HuracanFengshun | 910 | 0.027012 | 0.032031 | 0.021006 | -0.028008 | 0.004006 | -0.001053 | 0.015023 | 0.024023 | 0.012024 | -0.02501 | 0.038031 | -0.0305 | | AIFSLP | 3 | AIFSgaiaLPM | 5 | 0.051049 | 0.084074 | 0.031026 | 0.001042 | 0.015019 | -0.020 | -0.013012 | 0.01045 | 0.071058 | 0.063035 | 0.067054 | -0.012052 | | AIFS | 34 | AIFShera | 6 | 0.0504 | 0.06805 | 0.04031 | 0.012018 | 0.021024 | 0.007013 | 0.014019 | 0.013029 | 0.089071 | 0.039027 | 0.075048 | 0.021019 | | AIFS | 34 | AIFSthalassaAIFSgaia | 87 | 0.04037 | 0.061065 | 0.026017 | -0.01008 | 0.021006 | -0.006014 | 0.005007 | 0.006025 | 0.051052 | 0.037041 | 0.062036 | -0.013012 | | LPAIFS | 4 | LPMAIFSthalassa | 78 | 0.05036 | 0.073058 | 0.031018 | 0.046004 | 0.014022 | -0.009002 | -0.0040 | 0.038022 | 0.062049 | 0.03502 | 0.05704 | 0.051012 | | scienceAI | 5 | findforecast | 11 | 0.003001 | -0.002001 | -0.003 | 0.014019 | -0.012009 | 0.014007 | -0.02015 | 0.018028 | -0.006 | -0.003014 | -0.005014 | 0.012031 | | scienceAI | 5 | zephyr | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAI | 5 | ngcm | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguPlusPlus | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguParametricPrediction | 12 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKangu | 6 | KanguS2SEasyUQ | 35 | -1.041063 | -1.12154 | -0.975984 | -1.076074 | -0.862847 | -01.956001 | -0.919952 | -1.0.976 | -1.141177 | -1.066145 | -0.97957 | -1.369321 | | CliMA | 7 | CliMAWeather2 | 16 | -0.1513 | -0.156143 | -0.212178 | -0.15114 | -0.112084 | -0.007003 | -0.123112 | -0.182133 | -0.149147 | -0.214203 | -0.181156 | -0.136104 | | CliMA | 7 | CliMAWeather | 1922 | -0.215259 | -0.258303 | -0.205256 | -0.324327 | -0.201232 | -0.082119 | -0.192224 | -0.188166 | -0.243287 | -0.28734 | -0.246332 | -0.32437 | | WindBorne | 8 | WeatherMesh | 17 | -0.174166 | -0.129125 | -0.187163 | -0.315296 | -0.17414 | -0.396424 | -0.1922 | -0.208 | -0.124151 | -0.169163 | -0.166123 | -0.229226 | | FengWuW2S | 9 | FengWu2 | 18 | -0.21191 | -0.189198 | -0.337289 | -0.076043 | -0.208158 | -0.053026 | -0.252219 | -0.108117 | -0.083 | -0.305315 | -0.426358 | -0.245224 | | FengWuW2S | 9 | FengWu | 2119 | -0.246236 | -0.328332 | -0.228194 | -0.134111 | -0.107074 | -0.163172 | -0.165146 | -0.161139 | -0.324339 | -0.424427 | -0.32276 | -0.274278 | | NordicS2SHAPPY | 10 | NordicS2S1AZN | 20 | -0.237 | -0.155 | -0.255 | -0.314 | -0.274 | -0.449 | -0.279 | -0.173 | -0.214 | -0.21 | -0.183 | -0.249 | NordicS2S | 10 | NordicS2S3 | 23 | -0.364 | -0.376 | -0.359 | -0.335 | -0.329 | -0.519 | -0.292 | -0.316 | -0.433 | -0.454 | -0.291244 | -0.414 | NordicS2S | 10 | NordicS2S2 | 26 | 259 | -0.517255 | -0.547538 | -0.494299- | 0.448069 | -0.46072 | -0.555299 | -0.411303 | -0.535219 | -0.645316 | -0.491 | -0.436 | -0.618443 |
|
| Expand |
|---|
| title | Forecast window 2 (days 26 to 32) |
|---|
|
| Team name | Team rank | Model name | Model rank | Global | Tropics | NHem. ExTro. | SHem. ExTro. | NHem. Polar | SHem. Polar | Europe | N. Amer. | S. Amer. | Africa | Asia | Oceania |
|---|
| CMAandFDUMicroEnsemble | 1 | FengshunAdjustMicroDuet | 1 | 0.054046 | 0.102081 | -0.015001 | 0.02063 | -0.012009 | 0.015034 | -0.002031 | 0.054052 | 0.085077 | 0.08706 | 0.018022 | 0.06086 | | CMAandFDUMicroEnsemble | 1 | FengshunHybridStillLearning | 42 | 0.037043 | 0.069078 | -0.011002 | 0.027052 | -0.022005 | 0.028033 | -0.004031 | 0.052044 | 0.053066 | 0.055075 | 0.004019 | 0.001089 | | CMAandFDUMicroEnsemble | 1 | Huracan | Fengshun79 | 0.009 | 0.001016 | -0.007019 | 0.013054 | 0.019003 | -0.004018 | -0.032043 | 0.013051 | 0.024 | -0.012034 | -0.026016 | 0.007 | -0.06 | 031 | | CMAandFDU | MicroEnsemble | 2 | MicroDuetFengshunAdjust | 23 | 0.052033 | 0.091071 | 0.002004 | 0.05401 | -0.008009 | 0.032004 | -0.016003 | 0.045036 | 0.08406 | 0.062084 | 0.027011 | 0.076038 | | MicroEnsembleCMAandFDU | 2 | StillLearningFengshunHybrid | 34 | 0.049029 | 0.089059 | -0.001002 | 0.043024 | -0.003016 | 0.024018 | -0.01901 | 0.035048 | 0.07045 | 0.082058 | -0.023001 | 0.097023 | | MicroEnsembleCMAandFDU | 2 | HuracanFengshun | 713 | -0.01006 | 0.019002 | -0.016003 | 0.0480 | -0.0005 | -0.004047- | 0.022009 | 0.039024 | 0.028001 | -0.031015 | -0.011002 | -0.002046 | | LP | 3 | LPM | 5 | 0.025022 | 0.043037 | -0.004 | 0.02026 | 0.011012 | 0.017023 | -0.04 | 0.052056 | 0.038039 | 0.024021 | 0.012008 | -0.014013 | | AIFS | 4 | AIFSgaia | 6 | 0.017013 | 0.031032 | -0.014003 | -0.008012 | -0.008005 | 0.005008 | -0.008018 | 0.056051 | 0.057054 | -0.002001 | -0.006015 | -0.048008 | | AIFS | 4 | AIFShera | 8 | 0.003004 | 0.006013 | -0.001009 | -0.007015 | -0.014006 | 0.00701 | -0.017016 | 0.021024 | 0.055054 | -0.017011 | -0.017023- | 0.018007 | | AIFS | 4 | AIFSthalassa | 15 | -0.011016 | -0.037035 | -0.003008 | -0.01401 | 0.002 | 0.036023 | -0.01021 | 0.038037 | 0.011006 | -0.123113 | -0.017023 | -0.046013 | | scienceAIKITKangu | 5 | KanguPlusPlus | findforecast | 10 | 9 | -0.0 | -0.0010 | -0.0 | -0.0030 | -0.0220 | -0.0070 | -0.0120 | -0.0040 | -0.0240 | -0.0050 | -0.0080 | -0.0130.028 | | scienceAIKITKangu | 5 | zephyrKanguParametricPrediction | 119 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | scienceAI | 5 | ngcmzephyr | 119 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKanguscienceAI | 65 | KanguPlusPlusngcm | 119 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | -0.0 | | KITKanguscienceAI | 65 | KanguParametricPredictionfindforecast | 1114 | -0.001 | -0.0018 | -0.0009- | 0.0028- | 0.0004 | -0.0 | -0.001- | 0.0027 | -0.0024 | -0.0015 | -0.0025- | 0.0019 | | KITKangu | 65 | KanguS2SEasyUQ | 34 | -1.076086 | -1.179202 | -01.995004 | -01.993025 | -0.79284 | -1.065003 | -01.952014 | -1.141106 | -1.144168 | -1.266326 | -0.9955 | -1.119132 | | CliMA | 7 | CliMAWeather2 | 16 | -0.213186 | -0.245227 | -0.296245 | -0.159145 | -0.114086 | -0.016013 | -0.228206 | -0.197152 | -0.23522 | -0.334304 | -0.267226 | -0.22187 | | CliMA | 7 | CliMAWeather | 1821 | -0.27733 | -0.324389 | -0.271333 | -0.267368 | -0.281286 | -0.1642 | -0.232318 | -0.117109 | -0.3355 | -0.43149 | -0.371435 | -0.326416 | | FengWuW2S | 8 | FengWu2 | 17 | -0.23199 | -0.288261 | -0.306265 | 0.012029 | -0.111105 | 0.05509 | -0.111108 | -0.223226 | -0.127117 | -0.33433 | -0.389331 | -0.302178 | | FengWuW2S | 8 | FengWu | 1918 | -0.281255 | -0.4138 | -0.227205 | -0.07065 | -0.076064 | -0.155136 | -0.1109 | -0.211203 | -0.39537 | -0.503491 | -0.3252 | -0.346269 | | NordicS2SHAPPY | 9 | NordicS2S1AZN | 2019 | -0.288291 | -0.248328 | -0.288268 | -0.304688 | -0.387338- | 0.326021 | -0.392214 | -0.193396 | -0.25468 | -0.319227 | -0.326254 | -0.322569 | | NordicS2S | 910 | NordicS2S3NordicS2S1 | 2520 | -0.525305 | -0.659248 | -0.347334 | -0.276343 | -0.464387 | -0.554335 | -0.47548 | -0.29324 | -0.575289 | -0.71528 | -0.471333 | -0.679306 | | NordicS2S | 910 | NordicS2S2NordicS2S3 | 2725 | -0.589492 | -0.608577 | -0.615385 | -0.63298 | -0.553451 | -0.57559 | -0.702534 | -0.501267 | -0.732522 | -0.66694 | -0.568483 | -0.534559 | | HAPPYNordicS2S | 10 | AZNNordicS2S2 | 2127 | -0.299591 | -0.353615 | -0.252609 | -0.678591 | -0.335513 | -0.0603 | -0.21727 | -0.311465 | -0.489717 | -0.256631 | -0.28573 | -0.609545 |
|
Figures showing aggregated RPSSs for best-performing model from top 10 teams
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
Figures showing percentage of grid points with positive period-aggregated RPSSs
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
Figures showing observed conditions with respect to defined ERA5 climatology
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Near-surface air temperature (tas), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Mean sea level pressure (mslp), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 1 (days 19 to 25) |
|---|
|
 Image Removed  Image Added |
| Expand |
|---|
| title | Accumulated precipitation (pr), forecast window 2 (days 26 to 32) |
|---|
|
 Image Removed  Image Added |